The ability of our model to effectively learn the underlying heterogeneous data distribution is evaluated through the model's performance in imputing missing values in the training data as well as in the unseen test data, and through the model's ability in predicting future observations. We demonstrate the efficacy of our model on two modified MNIST datasets to simulate temporal and longitudinal datasets as well as on a real clinical dataset from the Parkinson’s Progression Markers Initiative (PPMI). We compare our model's performance against the Heterogeneous Incomplete Variational Autoencoder (HI-VAE) [1]} and the Longitudinal Variational Autoencoder (L-VAE) [2]}. HI-VAE is one of the state-of-the-art VAE-based models in heterogeneous data imputation. However, it does not have explicit support for longitudinal data. On the other hand, L-VAE has demonstrated its effectiveness to successfully model longitudinal data. However, L-VAE makes the strong assumption that the data is Gaussian distributed. Through our experiments, we demonstrate how an appropriate choice of likelihood distributions results in improved performance for temporal and longitudinal VAEs.
